Retinal Imaging and EED Provider Targeting Case Study

Client question

A client offering retinal imaging and diabetic eye screening solutions wanted to identify value-based care organizations and PCPs likely to need screening support. The client needed a provider-level market file rather than a broad market-size estimate.

Data foundation

FastHSR combined Medicare claims, beneficiary enrollment data, PCP attribution, organization crosswalks, value-based care participation data, and EED measure data where available. The analysis connected patient-level screening information to the PCPs and organizations accountable for quality performance.

• Beneficiary file: identify eligible diabetic populations and coverage periods.
• Provider attribution: assign beneficiaries to PCPs using visit patterns, plurality logic, or client-specified attribution rules.
• Organization mapping: connect PCPs to TINs, groups, ACOs, value-based care entities, and markets.
• EED measure data: identify denominator, numerator, and gap status for Diabetes Care Eye Exam.
• Service history: use claims to detect eye exam and retinal imaging-related utilization where needed.

EED opportunity logic

The analysis started with the EED denominator and numerator. FastHSR then created provider-level and organization-level opportunity measures that could be used for outreach, sales prioritization, and market planning.

• Eligible diabetic beneficiaries attributed to each PCP.
• EED denominator counts by PCP, organization, and market.
• EED numerator counts and completion rates where data supported measurement.
• Open screening gap counts for beneficiaries not meeting the measure.
• Opportunity rates normalized by attributed diabetic population size.
• Minimum-count filters and suppression handling for small cells.

Provider and VBC targeting

FastHSR translated quality-measure results into a practical targeting file. The goal was not only to identify low performance, but to identify provider and organization segments where a retinal imaging solution could be operationally relevant.

• Ranked PCP list by EED gap count and attributed diabetic population size.
• Provider group and TIN rollups for contracting and account planning.
• ACO and value-based care organization affiliations where available.
• Market and geography cuts for territory planning.
• Flags for providers with enough denominator volume to support outreach.
• Optional segmentation by plan type, organization type, and care model.

Quality checks

Because provider targeting can be sensitive to attribution and denominator construction, FastHSR built validation checks into the workflow before delivering ranked outputs.

• Confirm denominator construction and eligibility windows.
• Review numerator logic across claims and available measure files.
• Compare PCP attribution volume against expected provider panel size.
• Check organization crosswalks for PCPs with multiple affiliations.
• Apply minimum-volume rules so small panels were not overinterpreted.

Findings

The analysis found that screening opportunity was concentrated unevenly across providers, organizations, and markets. Some PCPs had large diabetic panels with meaningful EED gaps, while others had too little denominator volume to support reliable targeting.

The final output helped the client move from broad value-based care messaging to a prioritized list of providers and organizations where retinal imaging and screening support could be positioned around a measurable quality gap.

Deliverables

• PCP-level EED opportunity file.
• Provider group, TIN, ACO, and value-based care organization rollups.
• EED denominator, numerator, completion rate, and gap counts where data supported reporting.
• Ranked target lists for sales, partnership, and territory planning.
• Market-level summaries by geography and organization type.
• Documentation of attribution logic, EED measure logic, suppression rules, and quality checks.

Use cases

• Retinal imaging sales targeting for value-based care organizations.
• Diabetic eye exam quality gap analysis.
• PCP and provider group prioritization.
• ACO and risk-bearing organization outreach.
• Territory planning for screening and care-gap closure services.

Frequently asked questions

Why use EED data for retinal imaging targeting?

EED Diabetes Care Eye Exam is directly aligned with diabetic eye screening. It helps identify where screening gaps are measurable and which providers or organizations are accountable for closing them.

Why identify PCPs instead of only organizations?

Organization-level summaries help with account planning, but PCP-level data shows where eligible patients are concentrated and where screening workflows may need support.

Can the analysis support territory planning?

Yes. The same provider-level file can be summarized by geography, organization type, VBC affiliation, denominator volume, and screening gap count to support sales territory and partnership planning.

For retinal imaging provider targeting, EED quality gap analytics, or value-based care sales intelligence, please email us.

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